Hyperspectral image change detection based on active convolutional neural network and spatial–spectral affinity graph learning

Song, Ruoxi; Feng, Yining; Xing, Chengdi; Mu, Zhenhua; Wang, Xianghai

doi:10.1016/j.asoc.2022.109130

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…In other words, the labeling cost for training samples is too expensive for hyperspectral analysis [20], especially in hyperspectral CD. There have been a few attempts to address this challenge through active learning approaches [21], [22] or pseudo label strategy [48], they still face limitations due to the inevitable requirement of human participation. Consequently, these limitations continue to impact the performance of current deep learning models, restraining their ability to accurately detect changes in realworld scenarios.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

Huang,

Zhang,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current deep learning-based approaches often neglect critical feature interactions across temporal, spatial, and spectral domains. To address these challenges, we introduce a semisupervised model called the HyperMatch-based Cross Temporal and Spatial Interaction Transformer (CTSIT) for hyperspectral change detection. The key contributions of this study are as follows: 1) Introduction of the HyperMatch training schedule, which relies on weak-to-strong consistency, to enhance feature extraction using both labeled and unlabeled data. 2) Contribution of the Cross Temporal Bidirectional Attention (CTBA) module to emphasize bidirectional temporal interactions and resemblances. 3) Introduction of the Spatial and Spectral Attention (SSA) module, which includes the Dense Cross Spatial Attention (DCSA) and Spectral Attention (SA) modules, to capture longrange densely spatial interactions and internal spectral similarities. Extensive comparative experiments conducted on three mainstream hyperspectral change detection datasets confirm the effectiveness and superiority of the proposed HyperMatch-based CTSIT in utilizing both labeled and unlabeled samples. For instance, the training ablation demonstrates that this method significantly outperforms most existing state-of-the-art (SOTA) hyperspectral change detection methods, even with a significantly smaller number of samples.

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